Application of Particle Swarm Optimization Algorithm for Optimizing ANN Model in Recognizing Ripeness of Citrus

This paper shows findings of the application of Particle Swarm Optimization (PSO) algorithm in optimizing an Artificial Neural Network that could categorize between ripeness and unripeness stage of citrus suhuensis. The algorithm would adjust the network connections weights and adapt its values duri...

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2018-03, Vol.340 (1), p.12015
Main Authors: Rosli, Anis Diyana, Adenan, Nur Sabrina, Hashim, Hadzli, Abdullah, Noor Ezan, Sulaiman, Suhaimi, Baharudin, Rohaiza
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Citrus fruits
Model accuracy
Nondestructive testing
Particle swarm optimization
Visible spectrum
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Summary:This paper shows findings of the application of Particle Swarm Optimization (PSO) algorithm in optimizing an Artificial Neural Network that could categorize between ripeness and unripeness stage of citrus suhuensis. The algorithm would adjust the network connections weights and adapt its values during training for best results at the output. Initially, citrus suhuensis fruit's skin is measured using optically non-destructive method via spectrometer. The spectrometer would transmit VIS (visible spectrum) photonic light radiation to the surface (skin of citrus) of the sample. The reflected light from the sample's surface would be received and measured by the same spectrometer in terms of reflectance percentage based on VIS range. These measured data are used to train and test the best optimized ANN model. The accuracy is based on receiver operating characteristic (ROC) performance. The result outcomes from this investigation have shown that the achieved accuracy for the optimized is 70.5% with a sensitivity and specificity of 60.1% and 80.0% respectively.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/340/1/012015